Thermal control coating is an important means of ensuring that a spacecraft remains operational at high temperatures. Due to limitations regarding preparation technology and material properties, the mechanical properties of the conventional thermal control coatings still need to be improved. To solve this problem, nanostructured alumina coatings (NCs) and conventional alumina coatings (CCs) were prepared using plasma-spraying technology. The microscopic morphology, phase structure, hardness, and thermal control properties (solar absorptance (αs) and emissivity (ε)) of the nanostructured alumina coatings were investigated and compared with those of conventional alumina coatings. The results show that the NC has a higher hardness value (1168.8 HV) and that its reflectivity exceeds 75% in the wavelength range of 446–1586 nm, while a high degree of emissivity of 0.863–0.87 is still maintained at 300–393 K. Furthermore, the results show that these highly reflective properties are related to the phase composition and internal micromorphology of the NC, whereby the solar absorption of the coating is reduced due to the increase in the alpha phase content (21.4%), the high porosity (5.21%) and the nanoparticles favoring the internal scattering. All these properties can improve the performance of this CC coating with low solar absorptance (αs) and high emissivity (ε).
Because the threshing device of a combine harvester determines the harvesting level and threshing separation performance of a combine harvester, the analysis and study of the threshing device of a combine harvester is key to improving its performance. Based on the threshing device of a half-feed combine harvester, the simulation model of a discrete element threshing device is established in this paper. With the threshing drum rotation speed, feed volume, and concave sieve vibration frequency as the variable factors, the BP neural network model and linear regression equation model established for the loss rate and impurity content for two kinds of threshing performance indicators, respectively, and through the discrete element threshing performance test, two kinds of methods of threshing performance prediction are analyzed. The results show that the neural network and linear regression can be used for the threshing performance indicators, however, the BP neural network prediction effect has a better prediction precision, better reliability, and the trained neural network can be used in the general case of the threshing performance indicators. This provides a new idea for improving the threshing performance of a combine harvester.
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